Remote Sensing (RS)
Keyvan Mokhtari; Hooshang Asadi Harouni; Mohammad Ali Aliabadi; Somayeh Beiranvand
Abstract
Extended Abstract 1- IntroductionAlteration is the simplest, cheapest and most suitable means of mineral exploration. The best way to find changes is to use satellite data processing.Asadi and Tabatabaei (2007) have used band ratio processing methods and false color images by using selected principal ...
Read More
Extended Abstract 1- IntroductionAlteration is the simplest, cheapest and most suitable means of mineral exploration. The best way to find changes is to use satellite data processing.Asadi and Tabatabaei (2007) have used band ratio processing methods and false color images by using selected principal component processing (PCA) to identify the range of variations in different regions on Aster images. Gomez et al. (2005) visualized the lithological units of Namibian using the PCA algorithm on Aster data.The exposed rock units in Muteh mining area include a series of sedimentary, volcanic, and volcanic-clastic metamorphic rocks that extends from the green schist facies to the border of green schist and amphibolites along the northeast-southwest direction. These units have been repeatedly penetrated by alkaline intrusions, especially acid and granite (Rashidenjad, Omran et al., 2002).In general, the controlling elements of mineralization in Muteh area include structural factors (faults and fractures), alteration, and deformation. Field observations indicate the occurrence of vein mineralization and gold sulfide deposits in mylonite shear zones and fault zones in felsic to mafic metavolcanic host rocks.Gold mineralization is mainly concentrated in highly altered metariolites containing iron and copper sulfides and within fractures as veins and deposits. Alterations in silica, sericite, and carbonation are also observed along with these sediments, which are studied as exploration keys (Moritz et al., 2006).In this area, according to the lithology and distribution of alteration zones and the type of mineralization in Muteh gold mine, gold orogeny-type mineralizations are expected, which can be indirectly identified by recognizing the above alteration.2- Materials and methodsIn this study, Aster satellite images have been used to identify, discover and separate alteration zones in ENVI 5.3 software. Also, Landsat 8 satellite images have been utilized for general investigation and identification of hydrothermal alteration zones and expansion of iron oxide minerals, and Sentinel 2 satellite data due to better spatial and radiometric resolution than the above data has been applied to increase the spatial resolution of these data and the spatial accuracy of the map from the extracted changes.In order to validate between the field observations and spectral analysis, 24 rock samples were taken from the place of alteration, especially siliceous, argillic, and sercitic alteration around Senjedeh and Chah Khatoon deposits. 11 samples were sent to Zarazma laboratory for XRD analysis, and five samples were sent to Zarkavan Alborz Company’s laboratory for chemical analysis of 41 elements by ICP-MS method and gold element by Fire Assay method.3- ResultsConsidering the relationship between alteration zones and metal mineralization, it is very important to know and map these areas in the exploration of these deposits.The results and images show that the methods used in determining and separating the altered areas in Muteh exploratory area are acceptable and the optimal and effective methods in this research, SAM and MF, have been introduced.According to the field observations and surface sampling around Chah Khatoon and Senjedeh mineral deposits, as well as the investigation of changes, it was found that the most important changes in the region are: silicification, kaolinization, sericization, chlorination, alonation, pyrite, carbonation and so forth. This wide range shows the difference in intensity of alteration in different parts of the mineral reserve, which can be attributed to the system of joints, fractures and faults in the region.According to the available evidence, the metariolite rock is highly silicified in the tensile zones or in places with dense seams, and the pyrite particles in the context of these rocks have turned into iron hydroxide.4- DiscussionBy using satellite data processing, various data and information can be identified and extracted. Satellite data processing is done in two ways: visual and digital processing. By combining these two methods, the desired effects can be detected more accurately than the accuracy of satellite images. The visual method consists of preparing images of different color combinations by placing spectral bands in the red, green, and blue channels. Digital satellite image processing methods include band ratio, principal component analysis, least square regression method (Ls-Fit), spectral analysis, spectral angle mapping (SAM), and adaptive MF filter. The selection of the above methods was based on the type of information requested to extract data from images.Aster sensor images have no blue band (spectral range 0.4-0.5 µm) and the color composition of its VNIR bands is a standard RGB (1,2,3) false color composition. In this color combination, vegetation is seen in red. Since the study area is located in a relatively arid environment without vegetation, vegetation cover was avoided in the spectral analysis.The use and processing of Aster satellite data is one of the main features of this sensor; the use of unique spectral reflectance curves of alteration indicator minerals helped to identify and highlight these altered areas as well as finding the potential of areas prone to metal mineralization. Due to the high ability of Sentinel-2A images in identifying gossan and iron oxide ranges, the processing of these data was used to highlight these areas better.5- ConclusionAccording to the agreement of the results of geochemical and XRD studies with the distribution map of the alteration zones identified from the reference spectrum (USGS) and the spectral library (JPL), with the distribution map of lines and structural fractures of Muteh exploratory zone outside the pre-identified areas, new alteration zones were also introduced that require field research to confirm the results of stereo data processing.
Qhasem Keikhosravi; Shahriar Khaledi; Ameneh Yahyavi
Abstract
Introduction Foehn is thedecending of hot and dry air that occurs under certain conditions in the lee of a mountain range.In an adiabatic process, the humid air rises toward mountain peaks on the windward hillside. With sufficient humidity, it is saturated and thus, forms clouds or precipitation. ...
Read More
Introduction Foehn is thedecending of hot and dry air that occurs under certain conditions in the lee of a mountain range.In an adiabatic process, the humid air rises toward mountain peaks on the windward hillside. With sufficient humidity, it is saturated and thus, forms clouds or precipitation. In this way, it loses moisture, and passing over the lee of maintain, descends and heat upin an adiabatic process. Thus, the air in the lee side gets warmer and drier than the air in the windward hillside. Moving upward toward the mountain peak, the air loses temperature. At the mountain peak, the saturated air hasreached dew point temperature, and begins to rain to discharge its moisture. This dry air descends, and cross the leeward hillside with increasing velocity, and at the base of the mountain, its temperature is higher than the initial air temperature (Haji Mohammadi, 1396). Data& Methods In order to extract the frequency of days with foehn windsin the present study, daily temperature, relative and hourly humidity and wind speed were prepared for a 10-year statistical period (2015-2006) and then heat wave index was used to extract the number of days with foehn winds. To investigate the effect of foehn on thermal stress of plants using Landsat 8 OLI images, factors affecting thermal stress inplants,such as albedo, short wavelength radiations reaching the Earth surface, long wavelengthradiations emitted from the Earth surface, long wavelength radiations entering the earth surface, net radiation flux and soil heat flux were analyzed. ENVI 5.3 and Arc GIS 10.1 wereused to perform calculations and produce the aforementioned maps. Results&Discussion The present study was conducted to investigate thefoehn phenomenon in the west Alborz Mountains and its effect on the amount of thermal stress in the vegetation cover.First, the frequency of foehn wind occurrence in the statistical period of 2006 to 2015, in stations under study was extracted using wind direction, baldiindex (heat wave index) and increasing temperature and decreasing relative humidity compared to the previous day. In other words, days with temperature higher than 0 degree Celsius were considered as a heat wave. Based on wind direction, temperature increase and relative humidity decrease compared to the previous day (which in some cases is twice or even more), days are associated with foehn wind. In order to investigate the effect of foehn on thermal stressin plants, a sample of images with better atmospheric conditions (lacking clouds) collected by Landsat 8 OLI sensor on September 24, 2015 –in which foehn phenomenon had taken place-was received from the website of US Geological Survey (Earth Explorer)in the present study.The study area (West Alborz Mountains) was selected and cut out ofthese images and radiometric corrections were performed on the resulting images using ENVI 5.3 software. Afterwards, parameters like atmospheric thickness (atmospheric conductivity), Top of AtmosphereAlbedo, Earth’s surface albedo, Earthdistancefrom the Sun, solar altitude, Normalized difference vegetation index (NDVI), leaf area index (LAI), Fracture value, brightness temperature, ground surface temperature were determined and net radiation flux reaching vegetation cover and soil heat fluxwere calculated using these parameters. The output maps were produced in ARCGIS 10.1 environment. Conclusion According to the study sample (September 4, 2015), results indicated that areas with dense forest cover (eastern hillsides of the Alborz Range) receives the highest values of net radiation.The effect of foehn infiltration on these hillsides has increased the amount of radiation received up to 600 or 700 W / m 2. In contrast, the net radiation received on the downstream of thewindwardhillsides (western hillsides) is about 75 and at higher altitudes 150 W / m 2less than areas under the influence offoehn.Due to lower vegetation densityand lower heat transfer,soil heat flux in the western hillsides is much higher than the eastern hillsides.Most of windward hillsides has a heat flux of between 80 and 120 W / m2, while in leeward hillsides,sunlight is absorbed by the canopy and the soil heat flux is between 20 and 40 W / m2.Thus, most of solar radiation is used to raise the temperature around the vegetation crown, provide the necessary conditions for higher evaporation from the vegetation and create thermal stressin the vegetation organs. Therefore, descending of air mass on trees and plants causes severe evapotranspiration.This will lead to rapid drying of the leaves, which will cause thermal stress in the plant’s organs and intensify the likelihood of forest fires.
Javad Javdan; Mohammad Hossein Rezaei Moghaddam; Yousef Ebadi
Abstract
Extended Abstract
Introduction
Land surface temperature (LST) is one of the key parameters in environmental studies on local to global scales. Considering the limitations of local meteorological stations, remote sensing has opened a new horizon in collection of suchinformation. Recently, successful ...
Read More
Extended Abstract
Introduction
Land surface temperature (LST) is one of the key parameters in environmental studies on local to global scales. Considering the limitations of local meteorological stations, remote sensing has opened a new horizon in collection of suchinformation. Recently, successful launch of Landsat 8 with two thermal bands has provided a good opportunity for retrieving land surface temperature usingthermal remote sensing technology. Many studies had been performedwith the aim of retrieving land surface temperature, but available evidencesshow a significant calibration uncertainty inThermal Infrared Sensor (TIRS) of Landsat 8 band 11 and thus development of new studies based on onethermal band seems to be necessary. However, calibration documents issued by the United States Geological Survey (USGS) indicated uncertainty ofdata received from Band 11 Thermal Infrared Sensor (TIRS) of Landsat 8 and suggested using Band 10 data as a single spectral band for LST estimation.
Materials & Methods
In this study, mono-window algorithm with its three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature)has been developedunderan automated algorithmin MATLABand was used for Landsat 8 data.Thermal band 10 was used to estimate brightness temperature. Bands 4 and 5 were also used to calculate the NDVI. Retrieval of LST from Landsat 8 TIRS data is performed based on the premise that brightness temperature (Ti)can be computed for any pixel of Band 10 using the mono-window algorithm.Since the observed thermal radiance for Band 10 of Landsat 8 TIRS is stored and transferredasa digital number (DNs) with 16 digits between 0 and 65,535, it is possible toconvertthe DN value into thermal radiance and then convert radiance into brightness temperature.Ground emissivity is calculatedusing land cover patterns received from other bands of Landsat 8, and the other two parameters are estimated based on the local meteorologicaldata. Usually, obtaining an accurate estimate of ground emissivity is very difficult, and the atmospheric water vapor content is considered to be a sensitive parameter in traditional LST retrieval methods.
Results & Discussion
The algorithm has been successfully applied to Tabriz city in north west of Iran with the aim of analyzing spatial distribution of LST. After running the algorithm on the satellite images of the study area on July 18,2016, a lower land surface temperature was observed in green spaces with 1.2°C accuracy as compared to urban areas and wastelands. The lowest temperature in the study area was 20°C and the highest temperature was 53°C and mean temperature was 38.78°C.Results indicate that the algorithm candiscover natural urban heat islands accurately. Moreover, spatial distribution of LST in the region is quite well matched with the land covers. Successful application of the algorithm proves the efficiency of improved mono-window algorithm as a method used for retrieving LST from Landsat 8 data.
Conclusion
Compared to common methods,the proposed algorithm estimates land surface temperature with minimum requirement for user intervention, least possible time and an acceptable accuracy. Itgives researches an opportunity to easily compute LST and apply it in other studies, and thus it is a significant tool.
